GeneEffect: Gene effect in cancer cell lines

In acidgenomics/DepMapAnalysis: DepMap Analysis Utilities

Gene effect in cancer cell lines

Description

Gene effect in cancer cell lines

Usage

GeneEffect(
  dataset = c("depmap_public_21q2", "depmap_public_21q1", "depmap_public_20q4v2",
    "depmap_public_20q3", "depmap_public_20q2", "depmap_public_20q1",
    "sanger_project_score_2021_05", "sanger_project_score_2019_08", "demeter2_data_v6"),
  project = "default",
  scoringMethod = "default"
)

Arguments

dataset	character(1). DepMap dataset release name.
project	character(1). Project name. Defaults to a combination of multiple projects ("combined"): CRISPR: Broad DepMap Public, Sanger Project Score (e.g. for depmap_public_21q2). RNAi: Achilles, DRIVE, Marcotte (e.g. for demeter2_data_v6).
scoringMethod	character(1). Scoring method name to use. CRISPR: "chronos" (as of 2021 Q1) or "ceres" (e.g. depmap_public_21q2 dataset). RNAi: "demeter2" (e.g. demeter2_data_v6 dataset).

Value

GeneEffect.

Assays

• effect: Chronos or CERES data with principle components strongly related to known batch effects removed, then shifted and scaled per cell line so the median nonessential KO effect is 0 and the median essential KO effect is -1.

• probability: Probability that knocking out the gene has a real depletion effect using gene_effect.

Note

Updated 2021-07-19.

Examples

object <- GeneEffect()
print(object)

acidgenomics/DepMapAnalysis documentation built on Jan. 26, 2024, 8:21 p.m.